About The Role

The role drives the development and scaling of core machine learning systems, shifting focus from baseline experimentation to highly reliable production deployments. The team focuses on building low-latency inference pipelines, optimizing model architectures, and establishing robust MLOps patterns to support real-time user experiences.

This position collaborates closely with data platform engineers and product managers to translate complex business objectives into scalable ML solutions. The engineer will have ownership over the end-to-end pipeline, ensuring that models perform reliably under heavy production loads.

Key Responsibilities

Design and deploy production-grade machine learning models for classification, recommendation, and NLP tasks using PyTorch, TensorFlow, or scikit-learn
Develop scalable data pipelines and feature engineering processes using PySpark, SQL, and orchestration tools like Airflow
Implement MLOps workflows for automated model training, validation, deployment, and versioning utilizing MLflow and Kubeflow
Build and maintain low-latency inference APIs (FastAPI, Triton Inference Server) running in Docker containers on Kubernetes clusters
Set up continuous monitoring and alerting systems to track model performance, data drift, and latency anomalies in production environments
Collaborate on architectural reviews, establishing best practices for code quality, testing, and CI/CD pipelines across the ML organization

What We Are Looking For

3-6 years of professional experience as a Machine Learning Engineer or Software Engineer working on production ML systems
Proficiency in Python and deep familiarity with ML frameworks such as PyTorch, scikit-learn, and Hugging Face Transformers
Experience deploying and scaling models on AWS or GCP, using containerization (Docker, Kubernetes) and IaC (Terraform)
Strong background in software engineering principles, including API design, system architecture, and unit/integration testing
Solid understanding of ML fundamentals: hyperparameter tuning, evaluation metrics, and optimization of deep learning models
Bonus: Experience fine-tuning Large Language Models (LLMs) or implementing retrieval-augmented generation (RAG) pipelines in production

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Machine Learning Engineer